{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Classification with Pre-trained Imagenet CNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example is adapted from [Caffe's iPython Notebook example](http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb). We use pre-trained CNN model [converted](http://mochajl.readthedocs.org/en/latest/user-guide/tools/import-caffe-model.html) from Caffe's [Model Zoo](https://github.com/BVLC/caffe/wiki/Model-Zoo). Specifically, we use `bvlc_reference_caffenet`, which is the same model used in Caffe's own iPython Notebook example.\n",
    "\n",
    "This notebook is located in `examples/ijulia/ilsvrc12` under Mocha's source directory. If you want to run this example by yourself, you need to\n",
    "\n",
    "* Install [IJulia.jl](https://github.com/JuliaLang/IJulia.jl), which is a Julia backend for IPython. Of course, you also need to have Python and IPython installed.\n",
    "* Install [Images.jl](https://github.com/timholy/Images.jl), which we will use to read image files in this example.\n",
    "* (Optional) Install [Gadfly.jl](http://gadflyjl.org/), which will be used to plot the class probability prediction.\n",
    "* Download pre-trained CNN model. There is a shell script `get-model.sh` that you could run to download the pre-trained CNN model in HDF5 format converted from Caffe's original binary protocol buffer format.\n",
    "\n",
    "After all the preparation, you can start the notebook by executing the following command in this demo's source directory.\n",
    "\n",
    "```bash\n",
    "ipython notebook --profile julia\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constructing Convolutional Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use Mocha's native extension here to get faster convolution. If you prefer to disable it or use CUDA backend instead, please refer to [Mocha's document](http://mochajl.readthedocs.org/en/latest/user-guide/backend.html) for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
      "  likely near /Users/chiyuan/.julia/v0.4/Logging/src/Logging.jl:24\n",
      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
      "  likely near /Users/chiyuan/.julia/v0.4/Logging/src/Logging.jl:24\n",
      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
      "  likely near /Users/chiyuan/.julia/v0.4/Logging/src/Logging.jl:24\n",
      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
      "  likely near /Users/chiyuan/.julia/v0.4/Logging/src/Logging.jl:40\n",
      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
      "  likely near /Users/chiyuan/.julia/v0.4/Logging/src/Logging.jl:40\n",
      "WARNING: Base.String is deprecated, use AbstractString instead.\n",
      "  likely near /Users/chiyuan/.julia/v0.4/Logging/src/Logging.jl:40\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Configuring Mocha...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Recompiling stale cache file /Users/chiyuan/.julia/lib/v0.4/JLD.ji for module JLD.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * CUDA       disabled by default\n",
      " * Native Ext enabled (MOCHA_USE_NATIVE_EXT environment variable detected)\n",
      "Mocha configured, continue loading module...\n",
      "Loading native extension libmochaext.so...\n",
      "Native extension loaded\n",
      "DefaultBackend = Mocha.CPUBackend\n"
     ]
    }
   ],
   "source": [
    "ENV[\"MOCHA_USE_NATIVE_EXT\"] = \"true\"\n",
    "\n",
    "using Mocha\n",
    "\n",
    "backend = CPUBackend()\n",
    "init(backend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will define the network structure. This is directly adapted from Caffe's `bvlc_reference_caffenet` model definition. Please refer to Mocha's CIFAR-10 tutorial on how to translate Caffe's model definition to Mocha. This model takes 3-channel color images of size 256-by-256 and crop to take a 227-by-227 region. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28-Oct 10:12:30:INFO:root:Constructing net imagenet on Mocha.CPUBackend...\n",
      "28-Oct 10:12:30:INFO:root:Topological sorting 16 layers...\n",
      "28-Oct 10:12:30:INFO:root:Setup layers...\n",
      "28-Oct 10:12:32:INFO:root:Network constructed!\n",
      "************************************************************\n",
      "          NAME: imagenet\n",
      "       BACKEND: Mocha.CPUBackend\n",
      "  ARCHITECTURE: 16 layers\n",
      "............................................................\n",
      " *** Mocha.MemoryDataLayer(data)\n",
      "    Outputs ---------------------------\n",
      "          data: Blob(256 x 256 x 3 x 1)\n",
      "............................................................\n",
      " *** Mocha.CropLayer(crop)\n",
      "    Inputs ----------------------------\n",
      "          data: Blob(256 x 256 x 3 x 1)\n",
      "    Outputs ---------------------------\n",
      "       cropped: Blob(227 x 227 x 3 x 1)\n",
      "............................................................\n",
      " *** Mocha.ConvolutionLayer(conv1)\n",
      "    Inputs ----------------------------\n",
      "       cropped: Blob(227 x 227 x 3 x 1)\n",
      "    Outputs ---------------------------\n",
      "         conv1: Blob(55 x 55 x 96 x 1)\n",
      "............................................................\n",
      " *** Mocha.PoolingLayer(pool1)\n",
      "    Inputs ----------------------------\n",
      "         conv1: Blob(55 x 55 x 96 x 1)\n",
      "    Outputs ---------------------------\n",
      "         pool1: Blob(27 x 27 x 96 x 1)\n",
      "............................................................\n",
      " *** Mocha.LRNLayer(norm1)\n",
      "    Inputs ----------------------------\n",
      "         pool1: Blob(27 x 27 x 96 x 1)\n",
      "    Outputs ---------------------------\n",
      "         norm1: Blob(27 x 27 x 96 x 1)\n",
      "............................................................\n",
      " *** Mocha.ConvolutionLayer(conv2)\n",
      "    Inputs ----------------------------\n",
      "         norm1: Blob(27 x 27 x 96 x 1)\n",
      "    Outputs ---------------------------\n",
      "         conv2: Blob(27 x 27 x 256 x 1)\n",
      "............................................................\n",
      " *** Mocha.PoolingLayer(pool2)\n",
      "    Inputs ----------------------------\n",
      "         conv2: Blob(27 x 27 x 256 x 1)\n",
      "    Outputs ---------------------------\n",
      "         pool2: Blob(13 x 13 x 256 x 1)\n",
      "............................................................\n",
      " *** Mocha.LRNLayer(norm2)\n",
      "    Inputs ----------------------------\n",
      "         pool2: Blob(13 x 13 x 256 x 1)\n",
      "    Outputs ---------------------------\n",
      "         norm2: Blob(13 x 13 x 256 x 1)\n",
      "............................................................\n",
      " *** Mocha.ConvolutionLayer(conv3)\n",
      "    Inputs ----------------------------\n",
      "         norm2: Blob(13 x 13 x 256 x 1)\n",
      "    Outputs ---------------------------\n",
      "         conv3: Blob(13 x 13 x 384 x 1)\n",
      "............................................................\n",
      " *** Mocha.ConvolutionLayer(conv4)\n",
      "    Inputs ----------------------------\n",
      "         conv3: Blob(13 x 13 x 384 x 1)\n",
      "    Outputs ---------------------------\n",
      "         conv4: Blob(13 x 13 x 384 x 1)\n",
      "............................................................\n",
      " *** Mocha.ConvolutionLayer(conv5)\n",
      "    Inputs ----------------------------\n",
      "         conv4: Blob(13 x 13 x 384 x 1)\n",
      "    Outputs ---------------------------\n",
      "         conv5: Blob(13 x 13 x 256 x 1)\n",
      "............................................................\n",
      " *** Mocha.PoolingLayer(pool5)\n",
      "    Inputs ----------------------------\n",
      "         conv5: Blob(13 x 13 x 256 x 1)\n",
      "    Outputs ---------------------------\n",
      "         pool5: Blob(6 x 6 x 256 x 1)\n",
      "............................................................\n",
      " *** Mocha.InnerProductLayer(fc6)\n",
      "    Inputs ----------------------------\n",
      "         pool5: Blob(6 x 6 x 256 x 1)\n",
      "    Outputs ---------------------------\n",
      "           fc6: Blob(4096 x 1)\n",
      "............................................................\n",
      " *** Mocha.InnerProductLayer(fc7)\n",
      "    Inputs ----------------------------\n",
      "           fc6: Blob(4096 x 1)\n",
      "    Outputs ---------------------------\n",
      "           fc7: Blob(4096 x 1)\n",
      "............................................................\n",
      " *** Mocha.InnerProductLayer(fc8)\n",
      "    Inputs ----------------------------\n",
      "           fc7: Blob(4096 x 1)\n",
      "    Outputs ---------------------------\n",
      "           fc8: Blob(1000 x 1)\n",
      "............................................................\n",
      " *** Mocha.SoftmaxLayer(prob)\n",
      "    Inputs ----------------------------\n",
      "           fc8: Blob(1000 x 1)\n",
      "    Outputs ---------------------------\n",
      "          prob: Blob(1000 x 1)\n",
      "************************************************************\n",
      "\n"
     ]
    }
   ],
   "source": [
    "img_width, img_height, img_channels = (256, 256, 3)\n",
    "crop_size = (227, 227)\n",
    "batch_size = 1  # could be larger if you want to classify a bunch of images at a time\n",
    "\n",
    "layers = [\n",
    "  MemoryDataLayer(name=\"data\", tops=[:data], batch_size=batch_size,\n",
    "      transformers=[(:data, DataTransformers.Scale(scale=255)),\n",
    "                    (:data, DataTransformers.SubMean(mean_file=\"model/ilsvrc12_mean.hdf5\"))],\n",
    "      data = Array[zeros(img_width, img_height, img_channels, batch_size)])\n",
    "  CropLayer(name=\"crop\", tops=[:cropped], bottoms=[:data], crop_size=crop_size)\n",
    "  ConvolutionLayer(name=\"conv1\", tops=[:conv1], bottoms=[:cropped],\n",
    "      kernel=(11,11), stride=(4,4), n_filter=96, neuron=Neurons.ReLU())\n",
    "  PoolingLayer(name=\"pool1\", tops=[:pool1], bottoms=[:conv1],\n",
    "      kernel=(3,3), stride=(2,2), pooling=Pooling.Max())\n",
    "  LRNLayer(name=\"norm1\", tops=[:norm1], bottoms=[:pool1],\n",
    "      kernel=5, scale=0.0001, power=0.75)\n",
    "  ConvolutionLayer(name=\"conv2\", tops=[:conv2], bottoms=[:norm1],\n",
    "      kernel=(5,5), pad=(2,2), n_filter=256, n_group=2, neuron=Neurons.ReLU())\n",
    "  PoolingLayer(name=\"pool2\", tops=[:pool2], bottoms=[:conv2],\n",
    "      kernel=(3,3), stride=(2,2), pooling=Pooling.Max())\n",
    "  LRNLayer(name=\"norm2\", tops=[:norm2], bottoms=[:pool2],\n",
    "      kernel=5, scale=0.0001, power=0.75)\n",
    "  ConvolutionLayer(name=\"conv3\", tops=[:conv3], bottoms=[:norm2],\n",
    "      kernel=(3,3), pad=(1,1), n_filter=384, neuron=Neurons.ReLU())\n",
    "  ConvolutionLayer(name=\"conv4\", tops=[:conv4], bottoms=[:conv3],\n",
    "      kernel=(3,3), pad=(1,1), n_filter=384, n_group=2, neuron=Neurons.ReLU())\n",
    "  ConvolutionLayer(name=\"conv5\", tops=[:conv5], bottoms=[:conv4],\n",
    "      kernel=(3,3), pad=(1,1), n_filter=256, n_group=2, neuron=Neurons.ReLU())\n",
    "  PoolingLayer(name=\"pool5\", tops=[:pool5], bottoms=[:conv5],\n",
    "      kernel=(3,3), stride=(2,2), pooling=Pooling.Max())\n",
    "  InnerProductLayer(name=\"fc6\", tops=[:fc6], bottoms=[:pool5],\n",
    "      output_dim=4096, neuron=Neurons.ReLU())\n",
    "  InnerProductLayer(name=\"fc7\", tops=[:fc7], bottoms=[:fc6],\n",
    "      output_dim=4096, neuron=Neurons.ReLU())\n",
    "  InnerProductLayer(name=\"fc8\", tops=[:fc8], bottoms=[:fc7],\n",
    "      output_dim=1000)\n",
    "  SoftmaxLayer(name=\"prob\", tops=[:prob], bottoms=[:fc8])\n",
    "]\n",
    "\n",
    "net = Net(\"imagenet\", backend, layers)\n",
    "println(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "open(\"net.dot\", \"w\") do out net2dot(out, net) end\n",
    "run(pipeline(`dot -Tpng net.dot`, \"net.png\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "RGBA Images.Image with:\n",
       "  data: 579x1892 Array{ColorTypes.RGBA{FixedPointNumbers.UfixedBase{UInt8,8}},2}\n",
       "  properties:\n",
       "    imagedescription: <suppressed>\n",
       "    spatialorder:  x y\n",
       "    pixelspacing:  1 1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Images\n",
    "imread(\"net.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28-Oct 10:12:52:DEBUG:root:Loading parameters from HDF5 for layer conv1\n",
      "28-Oct 10:12:52:WARNING:root:Automatic converting saved filter from Float32 to Float64\n",
      "28-Oct 10:12:52:WARNING:root:Automatic converting saved bias from Float32 to Float64\n",
      "28-Oct 10:12:52:DEBUG:root:Loading parameters from HDF5 for layer conv2\n",
      "28-Oct 10:12:52:WARNING:root:Automatic converting saved filter from Float32 to Float64\n",
      "28-Oct 10:12:52:WARNING:root:Automatic converting saved bias from Float32 to Float64\n",
      "28-Oct 10:12:52:DEBUG:root:Loading parameters from HDF5 for layer conv3\n",
      "28-Oct 10:12:52:WARNING:root:Automatic converting saved filter from Float32 to Float64\n",
      "28-Oct 10:12:52:WARNING:root:Automatic converting saved bias from Float32 to Float64\n",
      "28-Oct 10:12:52:DEBUG:root:Loading parameters from HDF5 for layer conv4\n",
      "28-Oct 10:12:52:WARNING:root:Automatic converting saved filter from Float32 to Float64\n",
      "28-Oct 10:12:52:WARNING:root:Automatic converting saved bias from Float32 to Float64\n",
      "28-Oct 10:12:53:DEBUG:root:Loading parameters from HDF5 for layer conv5\n",
      "28-Oct 10:12:53:WARNING:root:Automatic converting saved filter from Float32 to Float64\n",
      "28-Oct 10:12:53:WARNING:root:Automatic converting saved bias from Float32 to Float64\n",
      "28-Oct 10:12:53:DEBUG:root:Loading parameters from HDF5 for layer fc6\n",
      "28-Oct 10:12:53:WARNING:root:Automatic converting saved weight from Float32 to Float64\n",
      "28-Oct 10:12:53:WARNING:root:Automatic converting saved bias from Float32 to Float64\n",
      "28-Oct 10:12:53:DEBUG:root:Loading parameters from HDF5 for layer fc7\n",
      "28-Oct 10:12:53:WARNING:root:Automatic converting saved weight from Float32 to Float64\n",
      "28-Oct 10:12:54:WARNING:root:Automatic converting saved bias from Float32 to Float64\n",
      "28-Oct 10:12:54:DEBUG:root:Loading parameters from HDF5 for layer fc8\n",
      "28-Oct 10:12:54:WARNING:root:Automatic converting saved weight from Float32 to Float64\n",
      "28-Oct 10:12:54:WARNING:root:Automatic converting saved bias from Float32 to Float64\n",
      "28-Oct 10:12:54:DEBUG:root:Init network imagenet\n"
     ]
    }
   ],
   "source": [
    "# load pre-trained network parameters\n",
    "\n",
    "using HDF5\n",
    "h5open(\"model/bvlc_reference_caffenet.hdf5\", \"r\") do h5\n",
    "  load_network(h5, net)\n",
    "end\n",
    "init(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classifying Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After loading the network parameters, we can now feed images to the network and read the output from the top softmax layer to get predictions. We provided a demo wrapper of the network as a simple image classifier interface in `tools/image-classifier.jl`. We will be using that wrapper in this demo.\n",
    "\n",
    "Before constructing the classifier, we load the class names get a better understanding the numerical predictions. The original network was trained on a 1000-class dataset. The classes used by Caffe's pre-trained model could be found in `synset_words.txt`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000-element Array{ASCIIString,1}:\n",
       " \"tench, Tinca tinca\"                                                                 \n",
       " \"goldfish, Carassius auratus\"                                                        \n",
       " \"great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias\"\n",
       " \"tiger shark, Galeocerdo cuvieri\"                                                    \n",
       " \"hammerhead, hammerhead shark\"                                                       \n",
       " \"electric ray, crampfish, numbfish, torpedo\"                                         \n",
       " \"stingray\"                                                                           \n",
       " \"cock\"                                                                               \n",
       " \"hen\"                                                                                \n",
       " \"ostrich, Struthio camelus\"                                                          \n",
       " \"brambling, Fringilla montifringilla\"                                                \n",
       " \"goldfinch, Carduelis carduelis\"                                                     \n",
       " \"house finch, linnet, Carpodacus mexicanus\"                                          \n",
       " ⋮                                                                                    \n",
       " \"acorn\"                                                                              \n",
       " \"hip, rose hip, rosehip\"                                                             \n",
       " \"buckeye, horse chestnut, conker\"                                                    \n",
       " \"coral fungus\"                                                                       \n",
       " \"agaric\"                                                                             \n",
       " \"gyromitra\"                                                                          \n",
       " \"stinkhorn, carrion fungus\"                                                          \n",
       " \"earthstar\"                                                                          \n",
       " \"hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa\"          \n",
       " \"bolete\"                                                                             \n",
       " \"ear, spike, capitulum\"                                                              \n",
       " \"toilet tissue, toilet paper, bathroom tissue\"                                       "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = open(\"synset_words.txt\") do s map(x -> replace(strip(x), r\"^n[0-9]+ \", \"\"), readlines(s)) end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caffe's CNN on imagenet is trained with BGR channel order, so we specify channel re-ordering from the standard RGB order. The image data from Images.jl is in the range [0,1]. According to Caffe's original tutorial, we also need to re-scale the image data from [0,1] to [0,255], and subtract the mean. But those are already handled in the network definition via data transformers attached to the data layer.\n",
    "\n",
    "The second parameter to the constructor of `ImageClassifier` is the name of the blob in the network that gives the final prediction. Here we are using the output of the softmax layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: `require` is deprecated, use `using` or `import` instead\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier constructed\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " in depwarn at deprecated.jl:73\n",
      " [inlined code] from deprecated.jl:694\n",
      " in require at no file:0\n",
      " in include_string at loading.jl:266\n",
      " in execute_request_0x535c5df2 at /Users/chiyuan/.julia/v0.4/IJulia/src/execute_request.jl:177\n",
      " in eventloop at /Users/chiyuan/.julia/v0.4/IJulia/src/IJulia.jl:141\n",
      " in anonymous at task.jl:447\n",
      "while loading In[7], in expression starting on line 1\n"
     ]
    }
   ],
   "source": [
    "require(joinpath(Pkg.dir(\"Mocha\"), \"tools/image-classifier.jl\"))\n",
    "classifier = ImageClassifier(net, :prob, channel_order=(3,2,1), classes=classes)\n",
    "println(\"Classifier constructed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "RGB4 Images.Image with:\n",
       "  data: 256x256 Array{ColorTypes.RGB4{FixedPointNumbers.UfixedBase{UInt8,8}},2}\n",
       "  properties:\n",
       "    imagedescription: <suppressed>\n",
       "    spatialorder:  x y\n",
       "    pixelspacing:  1 1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load image for prediction\n",
    "img = imread(\"images/cat256.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tabby, tabby cat\n"
     ]
    }
   ],
   "source": [
    "# get prediction\n",
    "prob, class = classify(classifier, img)\n",
    "println(class)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the predicted class is tabby, tabby cat. We can also plot the top 5 highest predicted probability. If you didn't install Gadfly.jl, this step could be omitted. Currently (before Julia's static compilation is implemented) it takes a very long time to load Gadfly.jl."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
       "<svg xmlns=\"http://www.w3.org/2000/svg\"\n",
       "     xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n",
       "     xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n",
       "     version=\"1.2\"\n",
       "     width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n",
       "     stroke=\"none\"\n",
       "     fill=\"#000000\"\n",
       "     stroke-width=\"0.3\"\n",
       "     font-size=\"3.88\"\n",
       ">\n",
       "<g class=\"plotroot xscalable yscalable\" id=\"fig-c260232bf799427c98461ab006a79330-element-1\">\n",
       "  <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"#000000\" stroke-opacity=\"0.000\" id=\"fig-c260232bf799427c98461ab006a79330-element-2\">\n",
       "    <text x=\"65.97\" y=\"88.39\" text-anchor=\"middle\" dy=\"0.6em\">x</text>\n",
       "  </g>\n",
       "  <g class=\"guide xlabels\" font-size=\"2.82\" font-family=\"'PT Sans Caption','Helvetica Neue','Helvetica',sans-serif\" fill=\"#6C606B\" id=\"fig-c260232bf799427c98461ab006a79330-element-3\">\n",
       "    <text x=\"20.63\" y=\"81.72\" text-anchor=\"middle\" dy=\"0.6em\">0</text>\n",
       "    <text x=\"35.75\" y=\"81.72\" text-anchor=\"middle\" dy=\"0.6em\">1</text>\n",
       "    <text x=\"50.86\" y=\"81.72\" text-anchor=\"middle\" dy=\"0.6em\">2</text>\n",
       "    <text x=\"65.97\" y=\"81.72\" text-anchor=\"middle\" dy=\"0.6em\">3</text>\n",
       "    <text x=\"81.09\" y=\"81.72\" text-anchor=\"middle\" dy=\"0.6em\">4</text>\n",
       "    <text x=\"96.2\" y=\"81.72\" text-anchor=\"middle\" dy=\"0.6em\">5</text>\n",
       "    <text x=\"111.31\" y=\"81.72\" text-anchor=\"middle\" dy=\"0.6em\">6</text>\n",
       "  </g>\n",
       "  <g class=\"guide colorkey\" id=\"fig-c260232bf799427c98461ab006a79330-element-4\">\n",
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       "f:e,unbind:b(this.node||document,d,e,f||this)}));return this};a[\"un\"+d]=A[\"un\"+d]=function(a){for(var b=this.events||[],e=b.length;e--;)if(b[e].name==d&&(b[e].f==a||!a)){b[e].unbind();b.splice(e,1);!b.length&&delete this.events;break}return this}})(v[y]);A.hover=function(a,b,d,e){return this.mouseover(a,d).mouseout(b,e||d)};A.unhover=function(a,b){return this.unmouseover(a).unmouseout(b)};var r=[];A.drag=function(b,d,f,h,n,p){function u(r,v,w){(r.originalEvent||r).preventDefault();this._drag.x=v;\n",
       "this._drag.y=w;this._drag.id=r.identifier;!q.length&&a.mousemove(e).mouseup(l);q.push({el:this,move_scope:h,start_scope:n,end_scope:p});d&&k.on(\"snap.drag.start.\"+this.id,d);b&&k.on(\"snap.drag.move.\"+this.id,b);f&&k.on(\"snap.drag.end.\"+this.id,f);k(\"snap.drag.start.\"+this.id,n||h||this,v,w,r)}if(!arguments.length){var v;return this.drag(function(a,b){this.attr({transform:v+(v?\"T\":\"t\")+[a,b]})},function(){v=this.transform().local})}this._drag={};r.push({el:this,start:u});this.mousedown(u);return this};\n",
       "A.undrag=function(){for(var b=r.length;b--;)r[b].el==this&&(this.unmousedown(r[b].start),r.splice(b,1),k.unbind(\"snap.drag.*.\"+this.id));!r.length&&a.unmousemove(e).unmouseup(l);return this}});C.plugin(function(a,v,y,C){y=y.prototype;var A=/^\\s*url\\((.+)\\)/,w=String,z=a._.$;a.filter={};y.filter=function(d){var f=this;\"svg\"!=f.type&&(f=f.paper);d=a.parse(w(d));var k=a._.id(),u=z(\"filter\");z(u,{id:k,filterUnits:\"userSpaceOnUse\"});u.appendChild(d.node);f.defs.appendChild(u);return new v(u)};k.on(\"snap.util.getattr.filter\",\n",
       "function(){k.stop();var d=z(this.node,\"filter\");if(d)return(d=w(d).match(A))&&a.select(d[1])});k.on(\"snap.util.attr.filter\",function(d){if(d instanceof v&&\"filter\"==d.type){k.stop();var f=d.node.id;f||(z(d.node,{id:d.id}),f=d.id);z(this.node,{filter:a.url(f)})}d&&\"none\"!=d||(k.stop(),this.node.removeAttribute(\"filter\"))});a.filter.blur=function(d,f){null==d&&(d=2);return a.format('<feGaussianBlur stdDeviation=\"{def}\"/>',{def:null==f?d:[d,f]})};a.filter.blur.toString=function(){return this()};a.filter.shadow=\n",
       "function(d,f,k,u,p){\"string\"==typeof k&&(p=u=k,k=4);\"string\"!=typeof u&&(p=u,u=\"#000\");null==k&&(k=4);null==p&&(p=1);null==d&&(d=0,f=2);null==f&&(f=d);u=a.color(u||\"#000\");return a.format('<feGaussianBlur in=\"SourceAlpha\" stdDeviation=\"{blur}\"/><feOffset dx=\"{dx}\" dy=\"{dy}\" result=\"offsetblur\"/><feFlood flood-color=\"{color}\"/><feComposite in2=\"offsetblur\" operator=\"in\"/><feComponentTransfer><feFuncA type=\"linear\" slope=\"{opacity}\"/></feComponentTransfer><feMerge><feMergeNode/><feMergeNode in=\"SourceGraphic\"/></feMerge>',\n",
       "{color:u,dx:d,dy:f,blur:k,opacity:p})};a.filter.shadow.toString=function(){return this()};a.filter.grayscale=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {b} {h} 0 0 0 0 0 1 0\"/>',{a:0.2126+0.7874*(1-d),b:0.7152-0.7152*(1-d),c:0.0722-0.0722*(1-d),d:0.2126-0.2126*(1-d),e:0.7152+0.2848*(1-d),f:0.0722-0.0722*(1-d),g:0.2126-0.2126*(1-d),h:0.0722+0.9278*(1-d)})};a.filter.grayscale.toString=function(){return this()};a.filter.sepia=\n",
       "function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"matrix\" values=\"{a} {b} {c} 0 0 {d} {e} {f} 0 0 {g} {h} {i} 0 0 0 0 0 1 0\"/>',{a:0.393+0.607*(1-d),b:0.769-0.769*(1-d),c:0.189-0.189*(1-d),d:0.349-0.349*(1-d),e:0.686+0.314*(1-d),f:0.168-0.168*(1-d),g:0.272-0.272*(1-d),h:0.534-0.534*(1-d),i:0.131+0.869*(1-d)})};a.filter.sepia.toString=function(){return this()};a.filter.saturate=function(d){null==d&&(d=1);return a.format('<feColorMatrix type=\"saturate\" values=\"{amount}\"/>',{amount:1-\n",
       "d})};a.filter.saturate.toString=function(){return this()};a.filter.hueRotate=function(d){return a.format('<feColorMatrix type=\"hueRotate\" values=\"{angle}\"/>',{angle:d||0})};a.filter.hueRotate.toString=function(){return this()};a.filter.invert=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncG type=\"table\" tableValues=\"{amount} {amount2}\"/><feFuncB type=\"table\" tableValues=\"{amount} {amount2}\"/></feComponentTransfer>',{amount:d,\n",
       "amount2:1-d})};a.filter.invert.toString=function(){return this()};a.filter.brightness=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\"/><feFuncG type=\"linear\" slope=\"{amount}\"/><feFuncB type=\"linear\" slope=\"{amount}\"/></feComponentTransfer>',{amount:d})};a.filter.brightness.toString=function(){return this()};a.filter.contrast=function(d){null==d&&(d=1);return a.format('<feComponentTransfer><feFuncR type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncG type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/><feFuncB type=\"linear\" slope=\"{amount}\" intercept=\"{amount2}\"/></feComponentTransfer>',\n",
       "{amount:d,amount2:0.5-d/2})};a.filter.contrast.toString=function(){return this()}});return C});\n",
       "\n",
       "]]> </script>\n",
       "<script> <![CDATA[\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "    if (typeof define === \"function\" && define.amd) {\n",
       "        // Define as an anonymous module\n",
       "        define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
       "            return factory(Snap);\n",
       "        });\n",
       "    } else {\n",
       "        // Browser globals (glob is window)\n",
       "        // Snap adds itself to window\n",
       "        glob.Gadfly = factory(glob.Snap);\n",
       "    }\n",
       "}(this, function (Snap) {\n",
       "\n",
       "var Gadfly = {};\n",
       "\n",
       "// Get an x/y coordinate value in pixels\n",
       "var xPX = function(fig, x) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return x * fig.node.viewBox.baseVal.width / client_box.width;\n",
       "};\n",
       "\n",
       "var yPX = function(fig, y) {\n",
       "    var client_box = fig.node.getBoundingClientRect();\n",
       "    return y * fig.node.viewBox.baseVal.height / client_box.height;\n",
       "};\n",
       "\n",
       "\n",
       "Snap.plugin(function (Snap, Element, Paper, global) {\n",
       "    // Traverse upwards from a snap element to find and return the first\n",
       "    // note with the \"plotroot\" class.\n",
       "    Element.prototype.plotroot = function () {\n",
       "        var element = this;\n",
       "        while (!element.hasClass(\"plotroot\") && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.svgroot = function () {\n",
       "        var element = this;\n",
       "        while (element.node.nodeName != \"svg\" && element.parent() != null) {\n",
       "            element = element.parent();\n",
       "        }\n",
       "        return element;\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotbounds = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x0: bbox.x,\n",
       "            x1: bbox.x + bbox.width,\n",
       "            y0: bbox.y,\n",
       "            y1: bbox.y + bbox.height\n",
       "        };\n",
       "    };\n",
       "\n",
       "    Element.prototype.plotcenter = function () {\n",
       "        var root = this.plotroot()\n",
       "        var bbox = root.select(\".guide.background\").node.getBBox();\n",
       "        return {\n",
       "            x: bbox.x + bbox.width / 2,\n",
       "            y: bbox.y + bbox.height / 2\n",
       "        };\n",
       "    };\n",
       "\n",
       "    // Emulate IE style mouseenter/mouseleave events, since Microsoft always\n",
       "    // does everything right.\n",
       "    // See: http://www.dynamic-tools.net/toolbox/isMouseLeaveOrEnter/\n",
       "    var events = [\"mouseenter\", \"mouseleave\"];\n",
       "\n",
       "    for (i in events) {\n",
       "        (function (event_name) {\n",
       "            var event_name = events[i];\n",
       "            Element.prototype[event_name] = function (fn, scope) {\n",
       "                if (Snap.is(fn, \"function\")) {\n",
       "                    var fn2 = function (event) {\n",
       "                        if (event.type != \"mouseover\" && event.type != \"mouseout\") {\n",
       "                            return;\n",
       "                        }\n",
       "\n",
       "                        var reltg = event.relatedTarget ? event.relatedTarget :\n",
       "                            event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
       "                        while (reltg && reltg != this.node) reltg = reltg.parentNode;\n",
       "\n",
       "                        if (reltg != this.node) {\n",
       "                            return fn.apply(this, event);\n",
       "                        }\n",
       "                    };\n",
       "\n",
       "                    if (event_name == \"mouseenter\") {\n",
       "                        this.mouseover(fn2, scope);\n",
       "                    } else {\n",
       "                        this.mouseout(fn2, scope);\n",
       "                    }\n",
       "                }\n",
       "                return this;\n",
       "            };\n",
       "        })(events[i]);\n",
       "    }\n",
       "\n",
       "\n",
       "    Element.prototype.mousewheel = function (fn, scope) {\n",
       "        if (Snap.is(fn, \"function\")) {\n",
       "            var el = this;\n",
       "            var fn2 = function (event) {\n",
       "                fn.apply(el, [event]);\n",
       "            };\n",
       "        }\n",
       "\n",
       "        this.node.addEventListener(\n",
       "            /Firefox/i.test(navigator.userAgent) ? \"DOMMouseScroll\" : \"mousewheel\",\n",
       "            fn2);\n",
       "\n",
       "        return this;\n",
       "    };\n",
       "\n",
       "\n",
       "    // Snap's attr function can be too slow for things like panning/zooming.\n",
       "    // This is a function to directly update element attributes without going\n",
       "    // through eve.\n",
       "    Element.prototype.attribute = function(key, val) {\n",
       "        if (val === undefined) {\n",
       "            return this.node.getAttribute(key);\n",
       "        } else {\n",
       "            this.node.setAttribute(key, val);\n",
       "            return this;\n",
       "        }\n",
       "    };\n",
       "\n",
       "    Element.prototype.init_gadfly = function() {\n",
       "        this.mouseenter(Gadfly.plot_mouseover)\n",
       "            .mouseleave(Gadfly.plot_mouseout)\n",
       "            .dblclick(Gadfly.plot_dblclick)\n",
       "            .mousewheel(Gadfly.guide_background_scroll)\n",
       "            .drag(Gadfly.guide_background_drag_onmove,\n",
       "                  Gadfly.guide_background_drag_onstart,\n",
       "                  Gadfly.guide_background_drag_onend);\n",
       "        this.mouseenter(function (event) {\n",
       "            init_pan_zoom(this.plotroot());\n",
       "        });\n",
       "        return this;\n",
       "    };\n",
       "});\n",
       "\n",
       "\n",
       "// When the plot is moused over, emphasize the grid lines.\n",
       "Gadfly.plot_mouseover = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    var keyboard_zoom = function(event) {\n",
       "        if (event.which == 187) { // plus\n",
       "            increase_zoom_by_position(root, 0.1, true);\n",
       "        } else if (event.which == 189) { // minus\n",
       "            increase_zoom_by_position(root, -0.1, true);\n",
       "        }\n",
       "    };\n",
       "    root.data(\"keyboard_zoom\", keyboard_zoom);\n",
       "    window.addEventListener(\"keyup\", keyboard_zoom);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    xgridlines.data(\"unfocused_strokedash\",\n",
       "                    xgridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "    ygridlines.data(\"unfocused_strokedash\",\n",
       "                    ygridlines.attribute(\"stroke-dasharray\").replace(/(\\d)(,|$)/g, \"$1mm$2\"));\n",
       "\n",
       "    // emphasize grid lines\n",
       "    var destcolor = root.data(\"focused_xgrid_color\");\n",
       "    xgridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"focused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", \"none\")\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // reveal zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 1.0}, 250);\n",
       "};\n",
       "\n",
       "// Reset pan and zoom on double click\n",
       "Gadfly.plot_dblclick = function(event) {\n",
       "  set_plot_pan_zoom(this.plotroot(), 0.0, 0.0, 1.0);\n",
       "};\n",
       "\n",
       "// Unemphasize grid lines on mouse out.\n",
       "Gadfly.plot_mouseout = function(event) {\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    window.removeEventListener(\"keyup\", root.data(\"keyboard_zoom\"));\n",
       "    root.data(\"keyboard_zoom\", undefined);\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\"),\n",
       "        ygridlines = root.select(\".ygridlines\");\n",
       "\n",
       "    var destcolor = root.data(\"unfocused_xgrid_color\");\n",
       "\n",
       "    xgridlines.attribute(\"stroke-dasharray\", xgridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    destcolor = root.data(\"unfocused_ygrid_color\");\n",
       "    ygridlines.attribute(\"stroke-dasharray\", ygridlines.data(\"unfocused_strokedash\"))\n",
       "              .selectAll(\"path\")\n",
       "              .animate({stroke: destcolor}, 250);\n",
       "\n",
       "    // hide zoom slider\n",
       "    root.select(\".zoomslider\")\n",
       "        .animate({opacity: 0.0}, 250);\n",
       "};\n",
       "\n",
       "\n",
       "var set_geometry_transform = function(root, tx, ty, scale) {\n",
       "    var xscalable = root.hasClass(\"xscalable\"),\n",
       "        yscalable = root.hasClass(\"yscalable\");\n",
       "\n",
       "    var old_scale = root.data(\"scale\");\n",
       "\n",
       "    var xscale = xscalable ? scale : 1.0,\n",
       "        yscale = yscalable ? scale : 1.0;\n",
       "\n",
       "    tx = xscalable ? tx : 0.0;\n",
       "    ty = yscalable ? ty : 0.0;\n",
       "\n",
       "    var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
       "\n",
       "    root.selectAll(\".geometry, image\")\n",
       "        .forEach(function (element, i) {\n",
       "            element.transform(t);\n",
       "        });\n",
       "\n",
       "    bounds = root.plotbounds();\n",
       "\n",
       "    if (yscalable) {\n",
       "        var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
       "        root.selectAll(\".xfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(xfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".ylabels\")\n",
       "            .transform(xfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var y = cy * scale + ty;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (xscalable) {\n",
       "        var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
       "        var xtrans = new Snap.Matrix().translate(tx, 0);\n",
       "        root.selectAll(\".yfixed\")\n",
       "            .forEach(function (element, i) {\n",
       "                element.transform(yfixed_t);\n",
       "            });\n",
       "\n",
       "        root.select(\".xlabels\")\n",
       "            .transform(yfixed_t)\n",
       "            .selectAll(\"text\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var cx = element.asPX(\"x\"),\n",
       "                        cy = element.asPX(\"y\");\n",
       "                    var st = element.data(\"static_transform\");\n",
       "                    unscale_t = new Snap.Matrix();\n",
       "                    unscale_t.scale(1/scale, 1, cx, cy).add(st);\n",
       "\n",
       "                    element.transform(unscale_t);\n",
       "\n",
       "                    var x = cx * scale + tx;\n",
       "                    element.attr(\"visibility\",\n",
       "                        bounds.x0 <= x && x <= bounds.x1 ? \"visible\" : \"hidden\");\n",
       "                    }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // we must unscale anything that is scale invariance: widths, raiduses, etc.\n",
       "    var size_attribs = [\"font-size\"];\n",
       "    var unscaled_selection = \".geometry, .geometry *\";\n",
       "    if (xscalable) {\n",
       "        size_attribs.push(\"rx\");\n",
       "        unscaled_selection += \", .xgridlines\";\n",
       "    }\n",
       "    if (yscalable) {\n",
       "        size_attribs.push(\"ry\");\n",
       "        unscaled_selection += \", .ygridlines\";\n",
       "    }\n",
       "\n",
       "    root.selectAll(unscaled_selection)\n",
       "        .forEach(function (element, i) {\n",
       "            // circle need special help\n",
       "            if (element.node.nodeName == \"circle\") {\n",
       "                var cx = element.attribute(\"cx\"),\n",
       "                    cy = element.attribute(\"cy\");\n",
       "                unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
       "                                                        cx, cy);\n",
       "                element.transform(unscale_t);\n",
       "                return;\n",
       "            }\n",
       "\n",
       "            for (i in size_attribs) {\n",
       "                var key = size_attribs[i];\n",
       "                var val = parseFloat(element.attribute(key));\n",
       "                if (val !== undefined && val != 0 && !isNaN(val)) {\n",
       "                    element.attribute(key, val * old_scale / scale);\n",
       "                }\n",
       "            }\n",
       "        });\n",
       "};\n",
       "\n",
       "\n",
       "// Find the most appropriate tick scale and update label visibility.\n",
       "var update_tickscale = function(root, scale, axis) {\n",
       "    if (!root.hasClass(axis + \"scalable\")) return;\n",
       "\n",
       "    var tickscales = root.data(axis + \"tickscales\");\n",
       "    var best_tickscale = 1.0;\n",
       "    var best_tickscale_dist = Infinity;\n",
       "    for (tickscale in tickscales) {\n",
       "        var dist = Math.abs(Math.log(tickscale) - Math.log(scale));\n",
       "        if (dist < best_tickscale_dist) {\n",
       "            best_tickscale_dist = dist;\n",
       "            best_tickscale = tickscale;\n",
       "        }\n",
       "    }\n",
       "\n",
       "    if (best_tickscale != root.data(axis + \"tickscale\")) {\n",
       "        root.data(axis + \"tickscale\", best_tickscale);\n",
       "        var mark_inscale_gridlines = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        var mark_inscale_labels = function (element, i) {\n",
       "            var inscale = element.attr(\"gadfly:scale\") == best_tickscale;\n",
       "            element.attribute(\"gadfly:inscale\", inscale);\n",
       "            element.attr(\"visibility\", inscale ? \"visible\" : \"hidden\");\n",
       "        };\n",
       "\n",
       "        root.select(\".\" + axis + \"gridlines\").selectAll(\"path\").forEach(mark_inscale_gridlines);\n",
       "        root.select(\".\" + axis + \"labels\").selectAll(\"text\").forEach(mark_inscale_labels);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "var set_plot_pan_zoom = function(root, tx, ty, scale) {\n",
       "    var old_scale = root.data(\"scale\");\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    // compute the viewport derived from tx, ty, and scale\n",
       "    var x_min = -width * scale - (scale * width - width),\n",
       "        x_max = width * scale,\n",
       "        y_min = -height * scale - (scale * height - height),\n",
       "        y_max = height * scale;\n",
       "\n",
       "    var x0 = bounds.x0 - scale * bounds.x0,\n",
       "        y0 = bounds.y0 - scale * bounds.y0;\n",
       "\n",
       "    var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
       "        ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
       "\n",
       "    tx += x0;\n",
       "    ty += y0;\n",
       "\n",
       "    // when the scale change, we may need to alter which set of\n",
       "    // ticks is being displayed\n",
       "    if (scale != old_scale) {\n",
       "        update_tickscale(root, scale, \"x\");\n",
       "        update_tickscale(root, scale, \"y\");\n",
       "    }\n",
       "\n",
       "    set_geometry_transform(root, tx, ty, scale);\n",
       "\n",
       "    root.data(\"scale\", scale);\n",
       "    root.data(\"tx\", tx);\n",
       "    root.data(\"ty\", ty);\n",
       "};\n",
       "\n",
       "\n",
       "var scale_centered_translation = function(root, scale) {\n",
       "    var bounds = root.plotbounds();\n",
       "\n",
       "    var width = bounds.x1 - bounds.x0,\n",
       "        height = bounds.y1 - bounds.y0;\n",
       "\n",
       "    var tx0 = root.data(\"tx\"),\n",
       "        ty0 = root.data(\"ty\");\n",
       "\n",
       "    var scale0 = root.data(\"scale\");\n",
       "\n",
       "    // how off from center the current view is\n",
       "    var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
       "        yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
       "\n",
       "    // rescale offsets\n",
       "    xoff = xoff * scale / scale0;\n",
       "    yoff = yoff * scale / scale0;\n",
       "\n",
       "    // adjust for the panel position being scaled\n",
       "    var x_edge_adjust = bounds.x0 * (1 - scale),\n",
       "        y_edge_adjust = bounds.y0 * (1 - scale);\n",
       "\n",
       "    return {\n",
       "        x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
       "        y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
       "    };\n",
       "};\n",
       "\n",
       "\n",
       "// Initialize data for panning zooming if it isn't already.\n",
       "var init_pan_zoom = function(root) {\n",
       "    if (root.data(\"zoompan-ready\")) {\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    // The non-scaling-stroke trick. Rather than try to correct for the\n",
       "    // stroke-width when zooming, we force it to a fixed value.\n",
       "    var px_per_mm = root.node.getCTM().a;\n",
       "\n",
       "    // Drag events report deltas in pixels, which we'd like to convert to\n",
       "    // millimeters.\n",
       "    root.data(\"px_per_mm\", px_per_mm);\n",
       "\n",
       "    root.selectAll(\"path\")\n",
       "        .forEach(function (element, i) {\n",
       "        sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
       "        if (sw > 0) {\n",
       "            element.attribute(\"stroke-width\", sw);\n",
       "            element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
       "        }\n",
       "    });\n",
       "\n",
       "    // Store ticks labels original tranformation\n",
       "    root.selectAll(\".xlabels > text, .ylabels > text\")\n",
       "        .forEach(function (element, i) {\n",
       "            var lm = element.transform().localMatrix;\n",
       "            element.data(\"static_transform\",\n",
       "                new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
       "        });\n",
       "\n",
       "    var xgridlines = root.select(\".xgridlines\");\n",
       "    var ygridlines = root.select(\".ygridlines\");\n",
       "    var xlabels = root.select(\".xlabels\");\n",
       "    var ylabels = root.select(\".ylabels\");\n",
       "\n",
       "    if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
       "    if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
       "    if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
       "    if (root.data(\"xtickscales\") === undefined) {\n",
       "\n",
       "        // index all the tick scales that are listed\n",
       "        var xtickscales = {};\n",
       "        var ytickscales = {};\n",
       "        var add_x_tick_scales = function (element, i) {\n",
       "            xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "        var add_y_tick_scales = function (element, i) {\n",
       "            ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
       "        };\n",
       "\n",
       "        if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
       "        if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
       "        if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
       "        if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
       "\n",
       "        root.data(\"xtickscales\", xtickscales);\n",
       "        root.data(\"ytickscales\", ytickscales);\n",
       "        root.data(\"xtickscale\", 1.0);\n",
       "    }\n",
       "\n",
       "    var min_scale = 1.0, max_scale = 1.0;\n",
       "    for (scale in xtickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    for (scale in ytickscales) {\n",
       "        min_scale = Math.min(min_scale, scale);\n",
       "        max_scale = Math.max(max_scale, scale);\n",
       "    }\n",
       "    root.data(\"min_scale\", min_scale);\n",
       "    root.data(\"max_scale\", max_scale);\n",
       "\n",
       "    // store the original positions of labels\n",
       "    if (xlabels) {\n",
       "        xlabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"x\", element.asPX(\"x\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    if (ylabels) {\n",
       "        ylabels.selectAll(\"text\")\n",
       "               .forEach(function (element, i) {\n",
       "                   element.data(\"y\", element.asPX(\"y\"));\n",
       "               });\n",
       "    }\n",
       "\n",
       "    // mark grid lines and ticks as in or out of scale.\n",
       "    var mark_inscale = function (element, i) {\n",
       "        element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
       "    };\n",
       "\n",
       "    if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
       "    if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "    if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
       "\n",
       "    // figure out the upper ond lower bounds on panning using the maximum\n",
       "    // and minum grid lines\n",
       "    var bounds = root.plotbounds();\n",
       "    var pan_bounds = {\n",
       "        x0: 0.0,\n",
       "        y0: 0.0,\n",
       "        x1: 0.0,\n",
       "        y1: 0.0\n",
       "    };\n",
       "\n",
       "    if (xgridlines) {\n",
       "        xgridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
       "                        pan_bounds.x0 = bounds.x1 - bbox.x;\n",
       "                    }\n",
       "                    if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
       "                        pan_bounds.x1 = bounds.x0 - bbox.x;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    if (ygridlines) {\n",
       "        ygridlines\n",
       "            .selectAll(\"path\")\n",
       "            .forEach(function (element, i) {\n",
       "                if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
       "                    var bbox = element.node.getBBox();\n",
       "                    if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
       "                        pan_bounds.y0 = bounds.y1 - bbox.y;\n",
       "                    }\n",
       "                    if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
       "                        pan_bounds.y1 = bounds.y0 - bbox.y;\n",
       "                    }\n",
       "                    element.attr(\"visibility\", \"visible\");\n",
       "                }\n",
       "            });\n",
       "    }\n",
       "\n",
       "    // nudge these values a little\n",
       "    pan_bounds.x0 -= 5;\n",
       "    pan_bounds.x1 += 5;\n",
       "    pan_bounds.y0 -= 5;\n",
       "    pan_bounds.y1 += 5;\n",
       "    root.data(\"pan_bounds\", pan_bounds);\n",
       "\n",
       "    root.data(\"zoompan-ready\", true)\n",
       "};\n",
       "\n",
       "\n",
       "// drag actions, i.e. zooming and panning\n",
       "var pan_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        root.data(\"dx\", 0);\n",
       "        root.data(\"dy\", 0);\n",
       "        root.data(\"tx0\", root.data(\"tx\"));\n",
       "        root.data(\"ty0\", root.data(\"ty\"));\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        dx /= px_per_mm;\n",
       "        dy /= px_per_mm;\n",
       "\n",
       "        var tx0 = root.data(\"tx\"),\n",
       "            ty0 = root.data(\"ty\");\n",
       "\n",
       "        var dx0 = root.data(\"dx\"),\n",
       "            dy0 = root.data(\"dy\");\n",
       "\n",
       "        root.data(\"dx\", dx);\n",
       "        root.data(\"dy\", dy);\n",
       "\n",
       "        dx = dx - dx0;\n",
       "        dy = dy - dy0;\n",
       "\n",
       "        var tx = tx0 + dx,\n",
       "            ty = ty0 + dy;\n",
       "\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
       "    }\n",
       "};\n",
       "\n",
       "var zoom_box;\n",
       "var zoom_action = {\n",
       "    start: function(root, x, y, event) {\n",
       "        var bounds = root.plotbounds();\n",
       "        var width = bounds.x1 - bounds.x0,\n",
       "            height = bounds.y1 - bounds.y0;\n",
       "        var ratio = width / height;\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        x = xscalable ? x / px_per_mm : bounds.x0;\n",
       "        y = yscalable ? y / px_per_mm : bounds.y0;\n",
       "        var w = xscalable ? 0 : width;\n",
       "        var h = yscalable ? 0 : height;\n",
       "        zoom_box = root.rect(x, y, w, h).attr({\n",
       "            \"fill\": \"#000\",\n",
       "            \"opacity\": 0.25\n",
       "        });\n",
       "        zoom_box.data(\"ratio\", ratio);\n",
       "    },\n",
       "    update: function(root, dx, dy, x, y, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var px_per_mm = root.data(\"px_per_mm\");\n",
       "        var bounds = root.plotbounds();\n",
       "        if (yscalable) {\n",
       "            y /= px_per_mm;\n",
       "            y = Math.max(bounds.y0, y);\n",
       "            y = Math.min(bounds.y1, y);\n",
       "        } else {\n",
       "            y = bounds.y1;\n",
       "        }\n",
       "        if (xscalable) {\n",
       "            x /= px_per_mm;\n",
       "            x = Math.max(bounds.x0, x);\n",
       "            x = Math.min(bounds.x1, x);\n",
       "        } else {\n",
       "            x = bounds.x1;\n",
       "        }\n",
       "\n",
       "        dx = x - zoom_box.attr(\"x\");\n",
       "        dy = y - zoom_box.attr(\"y\");\n",
       "        if (xscalable && yscalable) {\n",
       "            var ratio = zoom_box.data(\"ratio\");\n",
       "            var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
       "            var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
       "            dx = width * dx / Math.abs(dx);\n",
       "            dy = height * dy / Math.abs(dy);\n",
       "        }\n",
       "        var xoffset = 0,\n",
       "            yoffset = 0;\n",
       "        if (dx < 0) {\n",
       "            xoffset = dx;\n",
       "            dx = -1 * dx;\n",
       "        }\n",
       "        if (dy < 0) {\n",
       "            yoffset = dy;\n",
       "            dy = -1 * dy;\n",
       "        }\n",
       "        if (isNaN(dy)) {\n",
       "            dy = 0.0;\n",
       "        }\n",
       "        if (isNaN(dx)) {\n",
       "            dx = 0.0;\n",
       "        }\n",
       "        zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
       "        zoom_box.attr(\"width\", dx);\n",
       "        zoom_box.attr(\"height\", dy);\n",
       "    },\n",
       "    end: function(root, event) {\n",
       "        var xscalable = root.hasClass(\"xscalable\"),\n",
       "            yscalable = root.hasClass(\"yscalable\");\n",
       "        var zoom_bounds = zoom_box.getBBox();\n",
       "        if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
       "            return;\n",
       "        }\n",
       "        var plot_bounds = root.plotbounds();\n",
       "        var zoom_factor = 1.0;\n",
       "        if (yscalable) {\n",
       "            zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
       "        } else {\n",
       "            zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
       "        }\n",
       "        var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
       "            ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
       "        set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
       "        zoom_box.remove();\n",
       "    },\n",
       "    cancel: function(root) {\n",
       "        zoom_box.remove();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
       "    var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
       "    var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
       "    var drag_action = zoomable ? zoom_action :\n",
       "                      panable  ? pan_action :\n",
       "                                 undefined;\n",
       "    root.data(\"drag_action\", drag_action);\n",
       "    if (drag_action) {\n",
       "        var cancel_drag_action = function(event) {\n",
       "            if (event.which == 27) { // esc key\n",
       "                drag_action.cancel(root);\n",
       "                root.data(\"drag_action\", undefined);\n",
       "            }\n",
       "        };\n",
       "        window.addEventListener(\"keyup\", cancel_drag_action);\n",
       "        root.data(\"cancel_drag_action\", cancel_drag_action);\n",
       "        drag_action.start(root, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.update(root, dx, dy, x, y, event);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_drag_onend = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
       "    root.data(\"cancel_drag_action\", undefined);\n",
       "    var drag_action = root.data(\"drag_action\");\n",
       "    if (drag_action) {\n",
       "        drag_action.end(root, event);\n",
       "    }\n",
       "    root.data(\"drag_action\", undefined);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.guide_background_scroll = function(event) {\n",
       "    if (event.shiftKey) {\n",
       "        increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
       "        event.preventDefault();\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseover = function(event) {\n",
       "    this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_button_mouseout = function(event) {\n",
       "     this.select(\".button_logo\")\n",
       "         .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomout_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_zoomin_click = function(event) {\n",
       "    increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_track_click = function(event) {\n",
       "    // TODO\n",
       "};\n",
       "\n",
       "\n",
       "// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
       "// The constants a, b, and c are solved using the constraint that the function\n",
       "// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
       "var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return a * Math.exp(b * position) + c;\n",
       "}\n",
       "\n",
       "// inverse of scale_from_slider_position\n",
       "var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
       "    var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
       "        b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
       "        c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
       "    return 1 / b * Math.log((scale - c) / a);\n",
       "}\n",
       "\n",
       "var increase_zoom_by_position = function(root, delta_position, animate) {\n",
       "    var scale = root.data(\"scale\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\");\n",
       "    var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
       "    position += delta_position;\n",
       "    scale = scale_from_slider_position(position, min_scale, max_scale);\n",
       "    set_zoom(root, scale, animate);\n",
       "}\n",
       "\n",
       "var set_zoom = function(root, scale, animate) {\n",
       "    var min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"scale\");\n",
       "    var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
       "    if (animate) {\n",
       "        Snap.animate(\n",
       "            old_scale,\n",
       "            new_scale,\n",
       "            function (new_scale) {\n",
       "                update_plot_scale(root, new_scale);\n",
       "            },\n",
       "            200);\n",
       "    } else {\n",
       "        update_plot_scale(root, new_scale);\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "var update_plot_scale = function(root, new_scale) {\n",
       "    var trans = scale_centered_translation(root, new_scale);\n",
       "    set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
       "\n",
       "    root.selectAll(\".zoomslider_thumb\")\n",
       "        .forEach(function (element, i) {\n",
       "            var min_pos = element.data(\"min_pos\"),\n",
       "                max_pos = element.data(\"max_pos\"),\n",
       "                min_scale = root.data(\"min_scale\"),\n",
       "                max_scale = root.data(\"max_scale\");\n",
       "            var xmid = (min_pos + max_pos) / 2;\n",
       "            var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
       "            element.transform(new Snap.Matrix().translate(\n",
       "                Math.max(min_pos, Math.min(\n",
       "                         max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
       "    });\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
       "    var root = this.plotroot();\n",
       "    var min_pos = this.data(\"min_pos\"),\n",
       "        max_pos = this.data(\"max_pos\"),\n",
       "        min_scale = root.data(\"min_scale\"),\n",
       "        max_scale = root.data(\"max_scale\"),\n",
       "        old_scale = root.data(\"old_scale\");\n",
       "\n",
       "    var px_per_mm = root.data(\"px_per_mm\");\n",
       "    dx /= px_per_mm;\n",
       "    dy /= px_per_mm;\n",
       "\n",
       "    var xmid = (min_pos + max_pos) / 2;\n",
       "    var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
       "                   dx / (max_pos - min_pos);\n",
       "\n",
       "    // compute the new scale\n",
       "    var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
       "    new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
       "\n",
       "    update_plot_scale(root, new_scale);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
       "    this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
       "    var root = this.plotroot();\n",
       "\n",
       "    // keep track of what the scale was when we started dragging\n",
       "    root.data(\"old_scale\", root.data(\"scale\"));\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.zoomslider_thumb_dragend = function(event) {\n",
       "    this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
       "    event.stopPropagation();\n",
       "};\n",
       "\n",
       "\n",
       "var toggle_color_class = function(root, color_class, ison) {\n",
       "    var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
       "    var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
       "    if (ison) {\n",
       "        guides.animate({opacity: 0.5}, 250);\n",
       "        geoms.animate({opacity: 0.0}, 250);\n",
       "    } else {\n",
       "        guides.animate({opacity: 1.0}, 250);\n",
       "        geoms.animate({opacity: 1.0}, 250);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "Gadfly.colorkey_swatch_click = function(event) {\n",
       "    var root = this.plotroot();\n",
       "    var color_class = this.data(\"color_class\");\n",
       "\n",
       "    if (event.shiftKey) {\n",
       "        root.selectAll(\".colorkey text\")\n",
       "            .forEach(function (element) {\n",
       "                var other_color_class = element.data(\"color_class\");\n",
       "                if (other_color_class != color_class) {\n",
       "                    toggle_color_class(root, other_color_class,\n",
       "                                       element.attr(\"opacity\") == 1.0);\n",
       "                }\n",
       "            });\n",
       "    } else {\n",
       "        toggle_color_class(root, color_class, this.attr(\"opacity\") == 1.0);\n",
       "    }\n",
       "};\n",
       "\n",
       "\n",
       "return Gadfly;\n",
       "\n",
       "}));\n",
       "\n",
       "\n",
       "//@ sourceURL=gadfly.js\n",
       "\n",
       "(function (glob, factory) {\n",
       "    // AMD support\n",
       "      if (typeof require === \"function\" && typeof define === \"function\" && define.amd) {\n",
       "        require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
       "            factory(Snap, Gadfly);\n",
       "        });\n",
       "      } else {\n",
       "          factory(glob.Snap, glob.Gadfly);\n",
       "      }\n",
       "})(window, function (Snap, Gadfly) {\n",
       "    var fig = Snap(\"#fig-c6b1acad35d44d6c9082609c346269ab\");\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-4\")\n",
       "   .drag(function() {}, function() {}, function() {});\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-6\")\n",
       "   .data(\"color_class\", \"color_tabby,_tabby_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-7\")\n",
       "   .data(\"color_class\", \"color_tiger_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-8\")\n",
       "   .data(\"color_class\", \"color_lynx,_catamount\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-9\")\n",
       "   .data(\"color_class\", \"color_Egyptian_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-10\")\n",
       "   .data(\"color_class\", \"color_Persian_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-12\")\n",
       "   .data(\"color_class\", \"color_tabby,_tabby_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-13\")\n",
       "   .data(\"color_class\", \"color_tiger_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-14\")\n",
       "   .data(\"color_class\", \"color_lynx,_catamount\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-15\")\n",
       "   .data(\"color_class\", \"color_Egyptian_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-16\")\n",
       "   .data(\"color_class\", \"color_Persian_cat\")\n",
       ".click(Gadfly.colorkey_swatch_click)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-19\")\n",
       "   .init_gadfly();\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-23\")\n",
       "   .plotroot().data(\"unfocused_ygrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-23\")\n",
       "   .plotroot().data(\"focused_ygrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-118\")\n",
       "   .plotroot().data(\"unfocused_xgrid_color\", \"#D0D0E0\")\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-118\")\n",
       "   .plotroot().data(\"focused_xgrid_color\", \"#A0A0A0\")\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-281\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-281\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-281\")\n",
       "   .click(Gadfly.zoomslider_zoomin_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-285\")\n",
       "   .data(\"max_pos\", 97.31)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-285\")\n",
       "   .data(\"min_pos\", 80.31)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-285\")\n",
       "   .click(Gadfly.zoomslider_track_click);\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-287\")\n",
       "   .data(\"max_pos\", 97.31)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-287\")\n",
       "   .data(\"min_pos\", 80.31)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-287\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-287\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-287\")\n",
       "   .drag(Gadfly.zoomslider_thumb_dragmove,\n",
       "     Gadfly.zoomslider_thumb_dragstart,\n",
       "     Gadfly.zoomslider_thumb_dragend)\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-289\")\n",
       "   .data(\"mouseover_color\", \"#CD5C5C\")\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-289\")\n",
       "   .data(\"mouseout_color\", \"#6A6A6A\")\n",
       ";\n",
       "fig.select(\"#fig-c6b1acad35d44d6c9082609c346269ab-element-289\")\n",
       "   .click(Gadfly.zoomslider_zoomout_click)\n",
       ".mouseenter(Gadfly.zoomslider_button_mouseover)\n",
       ".mouseleave(Gadfly.zoomslider_button_mouseout)\n",
       ";\n",
       "    });\n",
       "]]> </script>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "Plot(...)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using Gadfly\n",
    "\n",
    "n_plot = 5\n",
    "n_best = sortperm(vec(prob), rev=true)[1:n_plot]\n",
    "best_probs = prob[n_best]\n",
    "best_labels = classes[n_best]\n",
    "\n",
    "plot(x=1:length(best_probs), y=best_probs, color=best_labels, Geom.bar, Guide.ylabel(\"probability\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the predicted class probability is highly concentrated. Here is another example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "RGB4 Images.Image with:\n",
       "  data: 256x256 Array{ColorTypes.RGB4{FixedPointNumbers.UfixedBase{UInt8,8}},2}\n",
       "  properties:\n",
       "    imagedescription: <suppressed>\n",
       "    spatialorder:  x y\n",
       "    pixelspacing:  1 1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img2 = imread(\"images/bird.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "indigo bunting, indigo finch, indigo bird, Passerina cyanea\n"
     ]
    }
   ],
   "source": [
    "prob, class = classify(classifier, img2)\n",
    "println(class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.4.0",
   "language": "julia",
   "name": "julia-0.4"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.4.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
